ranking algorithm 中文意思是什麼

ranking algorithm 解釋
排名演算法
  • ranking : n. 順序;【統計學】秩評定。adj. 第一流的;首席的;高級的;幹部的。 a ranking afficer 高級軍官。 a ranking player 一級選手。
  • algorithm : n. 【數學】演算法;規則系統;演段。
  1. It can dynamically determine the structure information need to index according to real query loads and opti mization of index. second, to consider the effects of structural information on result relevance ranking, this dissertation proposes a ranking algorithm that consider both the frequency distribution and structural distribution of keywords in the result, and a dynamic element - oriented method to compute the weight of keywords

    第二,為考慮xml數據中的結構信息對查詢結果相關度值的影響,本文提出了一種綜合考慮關鍵字頻率分佈特徵和結構分佈特徵的查詢結果相關度演算法,以及一種基於節點的關鍵字權重計演算法,取得了更優的檢索性能。
  2. Better segmentation effect can be attained by coding gray levels of pixels as eigenvector, taking advantage of histogram entropy principles function as fitness function, adopting ranking selection operation, making use of arithmetic crossover and mutation at a certain probability, combining with clustering analysis to initialize clustering center of the population to segment cells image with genetic algorithm

    以像素的灰度值為特徵向量進行編碼,利用直方圖熵法準則函數作為適應度函數,採用基於排名的選擇操作,以一定的概率進行算術交叉和變異,並結合聚類分析設定種群的聚類中心對細胞圖像進行遺傳聚類分割。
  3. First, a locating and ranking algorithm, based on theory of constraints ( toc ), is put forward. then, other methods aimed at different traffic measures of effectiveness are respectively advanced, which are the capacity method, delay method, speed method, investigation vehicle method, queue length method and density method respectively

    文章首先提出了基於約束理論( toc )的瓶頸定位與排序方法,然後針對具體的指標提出了通行能力法、延誤法、速度法、調查車法、車輛排隊長度法、密度法等六種方法。
  4. Ranking algorithm based on decision tree

    基於決策樹的排序學習演算法
  5. The goal of web information retrieval is to find all web pages for a user query in a collection of web pages and the task of ranking algorithm is evaluating related web pages for users

    Web信息檢索的目的是在網頁集合中找到與用戶查詢相關的所有網頁,而網頁評估演算法將對這些網頁進行評估后顯示給用戶。
  6. To manage large - scale xml documents with complicated structure, this dissertation focus on the efficient structural indexing algorithm for xml data, result size estimation problem for xml structure based query optimization, result relevance ranking algorithm, and infrastructure for xml query processing for both text - rich and data - rich xml documents. to address the aforementioned issues, this dissertation makes the following contributions. first, it investigates the drawbacks of existing indexing algorithm for xml data, and propose a dynamic indexing algorithm for xml data based on d - bisimilarity, difx

    為滿足結構復雜、大規模的xml數據管理需要,本文深入研究了xml信息檢索系統中的結構索引演算法設計和結構化查詢優化中的查詢代價估計問題,以及查詢結果和查詢條件間的相關度演算法,主要取得了4個方面的成果:第一,分析了已有的xml數據索引演算法中存在的問題,提出了一種高效的動態xml結構索引演算法difx ,它採用動態後向結構相似性( d - bisimilarity )的概念,可以根據實際查詢需求以及索引最優化的要求動態決定索引中保存的結構信息。
  7. Then a genetic algorithm ( ga ) was proposed using a real parameter coding, a ranking selection operator, an arithmetical crossover operator and a uniform mutation operator, and calculated the least - square error of the observed and computed values as its fitness function

    實例計算表明,雖然雲峰大壩存在一些安全隱患(諸如溫控不當導致的混凝土缺陷、壩基巖石中存在斷層等) ,但總的來講其運行狀況良好。
  8. Sliq algorithm is a fast and scalable classification algorithm for data mining, which is brought forward by ibm almaden research center in 1996. the typical application of sliq lies in crm, credit ranking, etc in large business

    Sliq串列演算法是由ibmalmaden研究中心提出的一種高速可伸縮的分類演算法,廣泛應用於大型商業的crm 、信用等級分級等領域。
  9. Conclusion the proposed algorithm can be used for ranking selection filter designing in the unstable signal processing applications, especially in the image processing

    結論演算法可以很好地用於圖像處理中的秩選擇濾波器設計。
  10. Recognize the current focused topic of interests to the active user as his navigational pattern. define the page recommendation ranking function, considering that whether a page has been visited by active user in his current session should be distinguished among pages to be suggested. design and implement a recommendation algorithm with high accuracy and low time

    4 .確定當前用戶感興趣的主題為該用戶的訪問模式;將候選推薦集中的web頁面是否已被當前用戶訪問過所帶來的推薦權值差異考慮其中,確定最終的推薦函數;設計並實現具有較高精確性及較低時間復雜度的推薦演算法。
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